Coefficient Of Correlation Value Research Articles

Prediction of cavitation using machine learning techniques on centrifugal pump

Abstract When the local static pressure drops below the fluid’s vapour pressure at that particular temperature, a dynamic process known as cavitation takes place in the liquid. Due to cavitation, the performance (total head and flow rate) of the pump will deteriorate, and hence the vibration and noise will develop. Both the performance and cavitation investigations are carried out for a low-specific-speed pump as per the testing standards. The results obtained during cavitation studies are extracted to train the machine learning algorithms. The flow rate, speed, torque, suction head, and delivery head are considered as input variables for machine learning, with total head, Net Positive Suction Head (NPSH), efficiency, and noise magnitude being output variables. The current studies employ four machine learning techniques: random forest, support vector machines, extreme gradient boosting, and linear regression. In the analysis, notable performance was observed for the random forest and extreme gradient boosting algorithms among others consistently demonstrated superior predictive capabilities for the output parameters of the pumps. For linear regression, random forest, and extreme gradient boosting, the coefficient correlation values are around one for all output parameters except for noise. Similar observation is depicted for coefficient of determination and normalized root mean squared error. These ensure the level of noise during cavitation is very dynamic. These findings will enable the pump user to incorporate AI techniques for the diagnosis of centrifugal pump operation with reference to the nominal flow rate (best efficiency flow). Through this technology, preventive and predictive maintenance of pumps can be adapted with less downtime in process industries, power plants, chemical industries, oil and gas refineries, and so on.

High-Strength Self-Compacting Concrete Production Incorporating Supplementary Cementitious Materials: Experimental Evaluations and Machine Learning Modelling

AbstractThis study investigates mechanical properties, durability performance, non-destructive testing (NDT) characteristics, environmental impact evaluation, and advanced machine learning (ML) modelling techniques employed in the analysis of high-strength self-compacting concrete (HSSCC) incorporating varying supplementary cementitious materials (SCMs) to develop sustainable building construction. The findings from the fresh characteristics test indicate that mixes’ optimal flowability and passing qualities can be achieved using different concentrations of marble powder (MP) alongside a consistent amount of silica fume (SF) and fly ash (FA). Moreover, the incorporation of 10% MP along with 10% FA and 20% SF in HSSCC significantly improved the compressive strength by 14.68%, while the splitting tensile strength increased by 15.59% compared to the reference mix at 56 days. While random forest (RF), gradient boosting (GB), and their ensemble models exhibit strong coefficient correlation (R2) values, the GB model demonstrates more precision, indicating reliable predicted outcomes of the mechanical properties. Following subsequent testing, it has been demonstrated that incorporating SCMs improves the NDT properties of HSSCC and enhances its durability. The finer MP, SF, and FA particles enhanced microstructural performance by minimizing voids and cracks while improving the C–H–S bond. As noticed by its lower CO2-eq per MPa for SCMs, the HSSCC mix with up to 15% MP inclusion increased mechanical strength while reducing the environmental footprint, making it an eco-friendly concrete alternative.

QSPR-based prediction model for the melting point of polycyclic aromatic hydrocarbons using MLR and ANN methods

The melting point is an important property that helps generate specific compounds with desired thermos-physical properties. Much work has been done applying quantitative structure-property relationships to improve the melting-point correlations, but they are unreliable. This gap might come from the melting point's sensitivity for small molecular variations and descriptors, which currently do not fully consider all factors determining melting behavior. In this work, we provide a QSPR model for predicting the melting point of a heterogeneous polycyclic aromatic hydrocarbons dataset. The model was generated using a robust hybrid linear approach (Genetic Algorithm-Multiple Linear Regression) and a nonlinear approach named Artificial Neural Network (ANN). Three descriptors were chosen to explain the influence of molecular weight and symmetry on melting point. The resulting QSPR model can model melting-point behavior with an RMSE of 34.88K, a coefficient correlation value of R²=0.887, and a prediction coefficient of Q²LOO= 0.863. This study reveals that the results produced by MLR were appropriate and served to predict melting points. However, compared to the results obtained by the ANN model, we conclude that the latter is more effective and better than the MLR model. Based on the results, our suggested model may be effective in predicting melting points, and the selected descriptors play essential roles in determining melting points.

PENGARUH INDUSTRI RUMAHAN KERAJINAN SANGKAR BURUNG TERHADAP PEREKONOMIAN MASYARAKAT SANGGRAAGUNG SOCAH BANGKALAN

This study was motivated by the researcher's curiosity about the influence of the home industry of bird cage crafts on improving society's economy in the village of Sanggra Agung, which has been doing these crafts for generations. Although they continuously produce bird cages, it seems that the economy of the bird cage craftsmen is still not growing. The study was carried out using a quantitative non-sampling method because the number of craftsmen was still below 100, and the data were obtained from the responses to the craftsmen's questionnaire and observations in the field about the home industry of bird cage crafts. From the results of the validity and reliability instrument tests, as well as the data normality assumption test, no deviations were found in the instrument or questionnaire data. Based on a simple regression analysis, it shows that the variable in the home industry of bird cage crafts affects the community economy, although the effect is not very significant. The coefficient correlation value is 0.211 or 4.5%, and the remaining 95.5% is influenced by other variables not discussed in this study.

Assessment of oceanographic services for the monitoring of highly anthropised coastal lagoons: The Mar Menor case study

Ocean monitoring systems are designed for continuous monitoring of the seas and oceans to track their evolution and thus anticipate environmental issues. However, they are often based on Internet of Things (IoT) systems that are expensive, hard to maintain, and offer little spatial coverage. An emerging alternative is Satellite Remote Sensing (SRS) systems that offer good geographical coverage but, as a reliable and real-time monitoring system, they also face several challenges such as environmental conditions, spatio-temporal granularity, terrain oleography, etc. This paper introduces an easy-to-use software tool to crawl water-quality data from up to 6 satellite instruments (i.e. Sentinel 3A, Sentinel-3B, NOAA VIIRS, SNPP VIIRS, Aqua MODIS, Terra MODIS) of the European Space Agency's (ESA) Coopernicus system and NASA's Marine Environmental Monitoring Service. We also provide an in-depth analysis in terms of reliability and data coverage for the chlorophyll-a (Chl-a) in a highly anthropised local/regional context like the Mar Menor lagoon (Murcia, Spain), where serious socio-environmental issues are arising due to the eutrophication process. Our results show a good linear correlation between in situ data, obtained by several Spanish public institutions since 2016, and data generated by the IRTM-NN (Inverse Radiative Transfer Model-Neural Network) algorithm of Sentinel 3 in general, reaching coefficient correlation values close to 0.9. They also show that organic matter inputs from ephemeral streams are relevant in determining Chl-a concentrations. Moreover, the temporal coverage using a single instrument is rather limited, as no data were recorded for 80% of the days studied. This figure decreases to 30% when data from the 6 satellite instruments are combined, increasing the temporal granularity of the measurements from approximately 5 to 1.5 days, suggesting the need for a combination of these systems for a robust SRS system.

Accuracy of a markerless system to estimate the position of taekwondo athletes in an official combat area

ABSTRACT Obtaining kinematic analysis through a gold-standard method on sports during official competitions is challenging. Markerless tracking system can be an alternative to provide physical and technical-tactical performance data about the athletes. Therefore, we aimed to verify whether the markerless system (OpenPose) accurately tracks taekwondo athletes’ positions on the official combat area. Six taekwondo athletes performed steps and kicks in a self-selected way in two conditions: with and without an opponent. To capture the movement, seven optoelectronic (marker-based system) and two digital cameras (OpenPose markerless system) were placed around the combat area at an acquisition frequency of 120 Hz and 30 Hz, respectively. Positions of the body centre of mass and midpoint of the feet were calculated and analysed through two and three-dimensional reconstruction both. The root mean square (RMS) error comparing both methods ranged from 0.13 to 0.32 m and 0.05 to 0.16 m for two and three-dimensional analysis, respectively. Bland-Altman analysis accepted the agreement between the capture systems, and the intraclass coefficient correlation (ICC) values were classified as excellent (ICC > 0.90) in all analysis. In conclusion, the OpenPose markerless system presents promising results for its use in tracking taekwondo athletes’ position on the competition area.

High-temperature effect on the mechanical behavior of recycled fiber-reinforced concrete containing volcanic pumice powder: An experimental assessment combined with machine learning (ML)-based prediction

The increasing volume of waste generated by various activities has increased interest in using waste to create sustainable construction materials to achieve possible benefits. In addition, using recycled materials to produce fresh concrete is a desirable option because of its low cost, lower landfill space requirement, and the completed concrete quality. Therefore, an experimental inquiry is undertaken to ascertain the impacts of up to 20 wt% cement displaced by Volcanic Pumice Powder (VPP) with the incorporation of 1% and 2% Recycled Nylon Fiber (RNF) on the mechanical properties of concrete composites following room temperature to high-temperature (600 °C) exposure. Fresh concrete characteristics tests were performed, including slump, compacting factor, Kelly ball penetration, and density. The heat resistance of the concrete was then measured by calculating the percentage decrease in weight, the splitting tensile strength, and the compressive strength of the specimens. Heating mainly raised VPP's pozzolanic reactivity and lowered high vapor pressure through melting RNF. Therefore, VPP and RNF-treated concrete had superior mechanical performance than control concrete even when exposed to elevated temperatures. Further, the microstructural modifications brought on by RNF and VPP additions were also explored by deploying Scanning Electron Microscopy (SEM). The use of VPP in concrete led to an improvement in fresh properties, while RNF demonstrated deterioration in the same qualities. Despite this, supervised machine learning techniques are a central focus of this investigation because of their potential to predict concrete characteristics accurately. To predict the fresh and mechanical characteristics of concrete, both the Random Forest (RF) and the K-Nearest Neighbors (KNN) algorithm, along with their ensemble model counterparts, were explored. The outcomes revealed that RNF and VPP considerably improved the concrete's heat resilience and mechanical characteristics and halted the concrete composites' explosive spalling behavior at 600 °C temperatures. To prevent strength loss at high temperatures, it was discovered that adding 1% RNF content to concrete with 10% VPP was the best combination. In addition, the high coefficient correlation (R2) value of the RF and the ensemble model indicates great accuracy in outcome prediction, while the low R2 value of the KNN model indicates that the KNN model is less accurate.

Bias Correction of IMERG Data in the Mountainous Areas of Sumatra Based on A Single Gauge Observation

The performance of surface precipitation data from satellite precipitation products (SPPs) in mountainous areas has greater error and bias than in plain areas. In this study, linear scaling (LS), local intensity (LOCI), power transformation (PT), and cumulative distribution function (CDF) methods are used to correct the bias of Integrated Multi-Satellite Retrievals for Global Precipitation Measurement (IMERG) data in the mountainous region of Sumatra based on long-term and high-resolution optical rain gauge (ORG) observations. The ORG is installed at Equatorial Atmospheric Observatory (EAO) in Kototabang, West Sumatra, Indonesia (100.32 °E, 0.20 °S, 865 m above sea level (ASL) with an observation period from 2002 to 2016. The impact of the bias correction method is tested based on accuracy and capability detection tests. The bias correction method is more effective at the daily resolution than the hourly resolution of the IMERG data in the mountainous region of Sumatra. The LS method exhibited the best improvement in accuracy with reduced root-mean-square error (RMSE) and relative bias (RB), although there was no significant increase in coefficient correlation (CC) values. However, the accuracy improvement was not observed in the bias correction for hourly data. The lack of improvement in the accuracy of the hourly IMERG data is due to the high local variability of rainfall in the mountainous area of Sumatra. The high data variability causes large differences in the mean and variance of the IMERG calibration and evaluation data periods. On the other hand, the LOCI, PT, and CDF methods were successfully improved the rain detection capability of IMERG, as indicated by the better critical succession index (CSI) values compared to the original hourly and daily IMERG data. It increased the CSI value by reducing false alarms for rain with intensity below 2 mm/h. Furthermore, the CDF method can improve the analysis of extreme rainfall in the mountainous region of Sumatra by improving the estimation of the extreme rainfall index. Therefore, these methods can be applied to improve the accuracy and detectability of IMERG data in the mountainous region of Sumatra. However, the scale factor and transfer function constructed in this study need to be further evaluated on other rain gauge observation data in Sumatra’s mountainous region to improve performance. HIGHLIGHTS The LS method shows the best improvement in the accuracy of IMERG data in the mountainous area of Sumatera compared to the LOCI, PT, and CDF methods, as indicated by the largest decrease in RMSE and RB values CSI values prove that LOCI, PT, and CDF methods successfully improve the detection capability of IMERG hourly and daily data in the mountainous region of Sumatra The CDF method shows the best quality in improving extreme rainfall observations in the mountainous region of Sumatra GRAPHICAL ABSTRACT

NIR Spectroscopy application to determine the carbohydrate and lipid content in green bean coffee

Coffee flavor is influenced by the chemical concentration including the carbohydrate and lipid content. Determination of the chemical content is still using the chemical method that spend time, expensive and destructive, then a suitable method which is fast and non destructive is required especially to fulfill the needs of coffee industries. This study aimed to apply NIRS method to determine the carbohydrate and lipid content from Indonesia green bean coffee from several origin to increase the data distribution, NIR wave of 1000-2500 nm, followed by determination of the carbohydrate and lipid content using chemical method. After that, NIR data pretreatment using multiple scatter correction (MSC), first and second derivative of savitzky golay, normalization and the combinations then calibrate both the spectra and chemical data by Partial Least Square (PLS) method. Result showed that NIR spectroscopy can be used to determine the carbohydrate and lipid content in green bean coffee could be seen by the high coefficient correlation, RPD and consistency value, while the CV is lower than 2.

Data‐Driven Decoupling Structural Feature Correlation for Harnessing Anionic Capacity in Na Layered Oxide

AbstractA data‐driven understanding of the reaction mechanism of the O3‐type Na[Li1/3Mn2/3]O2‐layered cathode model is systematically performed to decouple the complex correlations of covariate scale‐dependent variables in diagnosing and solving structural problems for facilitating nonhysteretic and reversible (nHR) oxygen capacities using interpretable machine learning (ML) assisted by density functional theory. A large dataset of vacancy formation energies depending on the desodiation mode for the oxide is investigated in detail, and it provides two numerical principles: i) linearizing the energy landscape and ii) steepening its slope to reach the ideal reaction. The heatmaps comprising Pearson coefficient correlation values are broken down into two‐scale components: i) macroscopic and ii) local structure features. Deriving the overall mitigation of scale‐dependent covariate variables in negative correlation potentially leads to nHR anionic redox upon (dis)charging. Containing the scale‐dependent features, the interpretable ML model based on a gradient‐boosting machine predicts each formation energy well. With data‐driven comprehension, honeycomb‐ and turtle‐type superstructures (TS) have been suggested depending on the thermodynamic (un)favorable pathways during desodiation from a local structural perspective, and the dangling O2– in the TS is a critical origin leading to the formation of O2 molecules trapped in the bulk.

Toxicological assessment of synthetic pesticides on physiology of Phaseolus vulgaris L. and Pisum sativum L. along with their correlation to health hazards: A case study in south-west Nagaland, India

Pesticide toxicity has been a major concern in the recent time. Higher concentrations of pesticides affect the organisms and environmental health. The present study aims to assess the toxicological effect of some commonly used pesticides in southwest Nagaland of India and their impact on the physiology of Phaseolus vulgaris and Pisum sativum along with their correlation to health hazards. The study area is a hilly region where the overall temperature ranges in between 2 °C and 35 °C. The annual rainfall received in the region is between 1000 and 2000 mm. The region shares a subtropical to tropical climate. Agriculture is the main activity of the region. Seeds of Phaseolus vulgaris and Pisum sativum were collected from the local market and sterilized with 2 % sodium hypochlorite for 15 min. The solution of monocrotophos, chlorpyrifos, dimethoate, dichlorvos, and cypermethrin was prepared with 1 ppm, 10 ppm, 50 ppm, and 100 ppm concentrations. The seeds were soaked for 24 h in the test solutions and kept uniformly in sterilized petri dishes in 10 ml of test solutions. For every experiment, three replicates of each sample were taken including the control. The control sample was run with tap water. For each replica 10 seeds were taken, so one sample contained 30 seeds. The seeds of the above two vegetable species were treated with these pesticides and analyzed for germination rate, phytotoxicity, dry biomass, and seedling vigor index. The result has shown the negative impact of higher concentrations of pesticides on plant physiology. A questionnaire and interview-based field survey were also conducted among the users to find out exposure-related health hazards of pesticides. The average time utilized for a single interview was 30 min. According to the self-assessed reports of 323 pesticide users, 17 physical disorders were recorded from the study. Eye irritation (49.12), excessive sweating (46.68), and excessive tiredness were some of the highly observed signs and symptoms among the users. For the statistical analysis, analysis of variance (ANOVA) for the biomass production in radicle and plumule of the studied species was done with OriginPro 8.5 software. A chi-square (χ2) test was performed at the significance level of 5 % for the comparative study among male and female farmers for exposure-related disorders. The correlation analysis was done with MS-EXCEL software. A coefficient correlation value of 0.54 was recorded, which indicates a positive correlation between the studied factors.

Relationship between Level of Knowledge and Anxiety Facing Menopause in Women in Karang Pule Village, West Nusa Tenggara, Indonesia

Significant hormonal changes during menopause can influence emotional and physical well-being. Some women experience anxiety due to hormonal changes that can influence mood, sleep, and energy. This study aims to describe the level of knowledge and anxiety about facing menopause in women in Karang Pule village, West Nusa Tenggara, Indonesia. This research is an observational study with a cross-sectional design. The research was carried out in August-November 2023 at the Integrated Development Post (Posbindu) in Karang Pule village, Mataram, West Nusa Tenggara, Indonesia. Participants in this research were menopausal women in Karang Pule village. The inclusion criteria for participants were women aged over 40 years, had experienced menopause, and were willing to be respondents. A total of 42 women took part in this study. The majority of female respondents in Karang Pule village have a high school education (47.62%), have sufficient knowledge (52.40%), and experience moderate anxiety (71.42%). There is a relationship between the level of maternal knowledge about menopause and the level of anxiety during menopause in Karang Pule village (p<0.05). A coefficient correlation value was also obtained -0.425, which means that the better the mother's knowledge about menopause, the lower the anxiety experienced. In conclusion, there is a relationship between the level of knowledge and the level of anxiety during menopause among respondents in Karang Pule Village.

UV- Spectrophotometric technique- based method development and validation for simultaneous estimation of Ciprofloxacin HCl and Quercetin in bulk powder

Antibiotic resistance is increasing worldwide, especially among ocular pathogens and one of the major reasons for this severity is the formation of biofilm which causes antibacterial specialists like Ciprofloxacin HCl to be ineffective, but combining it with substances that inhibit quorum sensing, a process that leads towards the development of biofilm, such as Quercetin, in a single formulation is an effective way to treat these infections. Aiming to develop and approve a new analytical method for the simultaneous evaluation of ciprofloxacin HCl and quercetin in their mass powder, the current examination is expected to accomplish this. Two strategies—the simultaneous equation approach (I) and the absorbance ratio method (II)—were created and validated in accordance with ICH guidelines for specificity, selectivity, linearity, precision, and accuracy. Quercetin's and Ciprofloxacin HCl's absorbance maxima (λmax) were determined to be at 327 nm and 271 nm, respectively, with coefficient correlation values of 0.999 and 0.997. Their isosbestic point was noticed at a maximum wavelength of 283 nm. Both Ciprofloxacin HCl and Quercetin demonstrated linearity in the focus range from 1 µg/ml to 10 µg/ml when absorbances were measured at any of the aforementioned levels. It was discovered that the developed processes were exact and precise with less than 2% relative standard deviation (%RSD). %Recovery studies were found to be 98.62-101.15% for Ciprofloxacin HCl at 271nm, 99.34-100.94% for Quercetin at 327nm, 99.63-104.62%, and 101.23-102.64% for Ciprofloxacin HCl and Quercetin respectively at their isosbestic point. Because it was discovered to be simple, rapid, specific, selective, linear, exact, and based on absorptivity measurements, the established UV Spectrophotometric technique can be used for in vitro depiction and contemporaneous assessment of Ciprofloxacin HCl and Quercetin.

Intelligent optimization for modeling carbon dioxide footprint in fly ash geopolymer concrete: A novel approach for minimizing CO2 emissions

In the global effort to mitigate climate change and reduce CO2 emissions, this study introduces an innovative, pioneering approach that combines artificial intelligence and experimental methods to investigate the CO2 footprint (CO2-FP) in fly ash geopolymer concrete materials. Three powerful non-linear intelligent learners, including Gaussian Process Regression (GPR) with Response Surface Methodology (RSM), Support Vector Regression (SVR), and Standalone Decision Tree Regression (DTR) are employed. The models are developed using seven input features related to the curing temperature, fly ash content, concentrations of coarse and fine aggregates, alkaline activators (Na2SiO3, NaOH) content, and superplasticizer. To identify the most influential input features, three different combinations (combo-1, combo-2, and combo-3) of these features are utilized in model building. The models' performance is assessed using key metrics such as coefficient of correlation (CC), Nash Sutcliffe coefficient efficiency (NSE), Mean Absolute Error (MAE), and Root Mean Square Error (RMSE). During the verification phase, the GPR-3 [Combo-3] model emerges as the most efficient in predicting the CO2-FP, with a high CC value of 0.9645 and NSE value of 0.9292. Consistently, Combo-3 demonstrates superior performance across all the models, underscoring the significance of the selected features. The findings of this study provide valuable guidance to industries and policymakers, enabling them to optimize concrete compositions and minimize CO2 emissions, thus contributing to global environmental sustainability.

Enhancing Nursing Graduate readiness for practice Through leadership competence: a correlational study

The sensitivity and complexity of healthcare sector mandate having competent leaders within nursing graduates. They are expected to hit the ground and be ready to lead their way when joining their first workplace. Although, some literatures have illuded to the correlation between leadership competency level and graduated nursing students’ readiness for practice, none of them studied the direct correlation among them. The purpose of this study was to investigate the degree of leadership competencies availability among nursing graduates of Jordanian nursing colleges and the degree of their readiness for practice, and the correlation among both variables. This study used a quantitative correlational method. 40 items questionnaire was developed for this study to assess leadership competency. while 22 items Casey- Fink survey was used to explore new graduates’ readiness for practice. The study sample was chosen based on convenience sampling technique and consisted of 268 Clinical Nurse Leaders (CNLs) who participate in supervising and preparing new graduating nurses in their first employment. The minimal required sample size was 260 participants, and the total number of obtained participants were 268. SPSS system was used to analyse the quantitative data in this study, frequencies, percentages, means and standard deviation were calculated for all study survey items. Pearson correlation coefficients was calculated to measure the relationship between the degree of leadership competencies availability among graduates of Jordanian nursing colleges and their readiness to practice, whereas Spearman's rho test used to measure the strength of association between leadership dimensions and new graduate’s readiness for practice. The overall results showed a moderate availability of leadership competencies, and a moderate degree of readiness to practice among nursing graduates from CNLs perspectives. Pearson coefficient correlation value showed a strong positive relationship among new graduates’ leadership competencies availability and their readiness for practice. To conclude, It is highly recommended to incorporate and focus on nursing leadership within teaching curriculum over different academic years in an incremental manner starting from fundamental kills, then to moderate and advance levels. More connections for students with the nursing professional body outside the university may start at early academic stages, connecting them with national nursing councils, Committees, and health organizations to improve their leadership competence and make them ready for workplace when graduating is highly recommended.

A simplified approach to hydraulic model calibration using HEC-RAS for water quality simulation Ciujung river

One approach to identifying the pollution load released by the industry quickly and accurately is to create a model taken from a reliable database. HEC-RAS was used in generating the Ciujung River water quality database to be used in simulating various scenarios in modeling water quality projections. In fact, the measurement data sometimes gives a misleading interpretation for understanding the relationship between hydraulics and water quality. Therefore, a simple approach to calibrating the hydraulic data is then developed. The effort to achieve mass conservation philosophy is taken into account in this research, such as bed elevation correlation, boundary condition adjustment downstream and upstream, Manning correction, and justification of flow events according to the limitations of measurement data. The result shows that the proposed simple approach in this research gives a value of coefficient correlation 0,9, at which mass conservation is achieved.

Modelling above-ground biomass estimation using combination of vegetation height and forest canopy density at secondary peat swamp forest of Kalimantan

Rapid estimation of carbon stock, particularly carbon from Above-Ground Biomass (AGB) in a high-potential carbon forest, is required to assess potential carbon emission projects. Satellite imagery has been widely used as the main data source to rapidly estimate AGB. Canopy Height (CH) data from NASA’s Global Ecosystem Dynamics Investigation (GEDI) and Forest Canopy Density (FCD) data from satellite Landsat 8 imagery are widely used to estimate AGB. An allometric model with multiple linear regression equations has been used to estimate the AGB value using both parameters of the FCD value and the CH value. 43 values of calculated AGB by non-destructive field survey located in a peat swamp forest area use as a training dataset to develop an allometric model. The CH and FCD indices performed several tests, and the test result met the requirements for developing allometric equations. The coefficient correlation (r) of CH and AGB from 43 sample plots is 0.546, indicating a moderate correlation. The coefficient correlation (r) for FCD and AGB is 0.267 which indicates a weak correlation. The validation test was carried out to assess AGB from the developed allometric model and AGB of field survey with a non-destructive method. The validation result of the allometric model has the value of coefficient correlation (r), RMSE, and R2, respectively 0.569; 22.235; and 0.324. In terms of allometric model validation, another forest with typical conditions, particularly basal area, the model’s AGB result, and the validated area have RMSE 46.506 and r value 0.664.

Improving Learning Outcomes Through Interactive Learning Media Based on Macromedia Flash

In order to adapt to the curriculum changes from 2016 to 2020 in the Air Navigation Engineering D3 Program, an innovative approach utilizing interactive learning media is required. This aims to support curriculum transformation due to technological advancements and the needs of the professional world. Therefore, the objective of this research is to assess the improvement of learning outcomes through the use of interactive learning media in teaching about navigation aids. The research subjects are D3 Air Navigation Engineering 13th Alpha and Bravo cohorts. Data collection was obtained from learning outcomes through Evaluation and Interactive Learning Media on Flight Navigation Aids. This research is a quasi-experimental quantitative study using pretest-posttest design. The conclusion drawn from the review and data analysis, to assess the validity using SPSS, is that the calculated r-value (r) is greater than the critical r-value (r 0.30), indicating that the questionnaire items are valid. Meanwhile, for the reliability test results, a Guttman SplitHalf coefficient correlation value of 0.929 was obtained, which is ≥ 0.80. The Cronbach's Alpha value was 0.861. It can be concluded that all items of the ability variable (x) are valid. The pre-test and post-test scores in the control group were 40 and 75, respectively. Whereas in the experimental group, the class provided with interactive learning media obtained pre-test and post-test scores of 45 and 85, respectively. Therefore, this learning media can lead to increased student engagement and a significant improvement in their academic success

Coconut Shell Charcoal Combination with Teakwood Sawdust (CSC-TS): An Effective Low-Cost Adsorbent for the Removal Cr(VI) Ion on Industrial Waste

The main goals of this study were to develop activated carbon (AC) from coconut shell charcoal (CSC) combination with teakwood sawdust (TS), combination of both on this study was known with (CSC-TS) as a pledging alternative treatment for perilous Cr(VI) metal from industrial waste which investigated with Batch method. Both AC treated differently, coconut shell charcoal prepared with three stages include dehydration with microwave assisted at 373 K for 24 hours, carbonization under 773 K for 1 hour and followed by chemical activation using H3PO4 10% for 24 hours. Meanwhile, teakwood sawdust prepared only with two stages include dehydration and chemical activation without carbonization. Batch adsorption process in this study did with the aqueous solutions and applied on batik industrial waste, the functional groups of CSC-TS was characterized using FTIR, furthermore the determination of some parameters in adsorption such as adsorbent ratio, effect of contact time, and the effectiveness optimum condition on batik industrial waste obtained by AAS. Based on FTIR result, several functional groups such as (OH) were detected. Langmuir and Freundlich models were calculated to find out the fit adsorption isotherms where Langmuir models had the greater coefficient correlation value (R2=0,9976) which represented the best isotherm models. The result showed that the Cr(VI) adsorption process was found to be maximum at GT-2,5 with 100 minutes of contact time. The percentage removal of Cr(VI) in aqueous solutions under optimal conditions respectively were 87 and 99% with the maximum Langmuir capacity adsorption was 1,08 mg g-1. The initial concentration of batik industrial waste on upstream and downstream were 0,593 and 0,779 mg L-1 with the percentage removal of Cr(VI) were respectively 72 and 83%. This batch adsorption study confirmed that the contaminant of Cr(VI) on industrial waste can be decreased by low-cost adsorbent CSC-TS.

Effectiveness of modified nitrogen fertilizer on soil chemical properties and rice plant growth in the textile industrial area

The imbalance of nutrients and heavy metal contamination is a challenge in increasing plant growth surround the textile industry area. This study aimed to determine the effectiveness of the dosage of Biologically Agent N Organo Mineral Fertilizer (Biomix-N) as a fertilizer and an ameliorant in paddy soil contaminated with textile liquid waste. This study used a randomized block design of eight treatments with four replications. Parameters observed were soil chemical properties after application of Biomix-N (Na-ex, total N, EC, Cd, and pH), rice plant growth (plant height and tiller number), Cd concentration in the plant, and relative agronomic effectiveness (RAE). Data analysis used analysis of variance, Duncan's test at a 5% level, regression, and correlation analysis. The results showed that Biomix-N 500 kg ha-1 affected the value of EC and total N, while it did not affect Na-ex, Cd, pH and Cd uptake by the paddy plant. Biomix-N fertilization affects the height and number of tillers of rice plants at the age of 70 DAP. Biomix-N fertilization 500 kg ha-1 gave an RAE value of 126-176% compared to the control treatment. The total N content of the soil has a high value of coefficient determinant and correlation (R2= 0.76; R = 0.9) on the growth of rice plants. Applying Biomix-N 500 kg ha-1 equal to 300 kg ha-1 of urea was an effective dose for paddy soil surround the textile industrial area.Keywords: Cadmium, nutrient balance, soil amendment